issue_comments

Table actions

1 row where author_association = "NONE" and user = 1411265 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

Suggested facets: created_at (date), updated_at (date)

user 1

  • keltonhalbert · 1 ✖

author_association 1

  • NONE · 1 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
561900194 https://github.com/pydata/xarray/issues/1385#issuecomment-561900194 https://api.github.com/repos/pydata/xarray/issues/1385 MDEyOklzc3VlQ29tbWVudDU2MTkwMDE5NA== keltonhalbert 1411265 2019-12-04T23:57:07Z 2019-12-04T23:57:07Z NONE

So is there any word on a best practice, fix, or workaround with the MFDataset performance? Still getting abysmal reading perfomance with a list of NetCDF files that represent sequential times. I want to use MFDataset to chunk multiple time steps into memory at once but its taking 5-10 minutes to construct MFDataset objects and even longer to run .values on it. 

{
    "total_count": 1,
    "+1": 1,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   slow performance with open_mfdataset 224553135

Advanced export

JSON shape: default, array, newline-delimited, object

CSV options: 

CREATE TABLE [issue_comments] (
   [html_url] TEXT,
   [issue_url] TEXT,
   [id] INTEGER PRIMARY KEY,
   [node_id] TEXT,
   [user] INTEGER REFERENCES [users]([id]),
   [created_at] TEXT,
   [updated_at] TEXT,
   [author_association] TEXT,
   [body] TEXT,
   [reactions] TEXT,
   [performed_via_github_app] TEXT,
   [issue] INTEGER REFERENCES [issues]([id])
);
CREATE INDEX [idx_issue_comments_issue]
    ON [issue_comments] ([issue]);
CREATE INDEX [idx_issue_comments_user]
    ON [issue_comments] ([user]);